LATIS offers a series of workshops created by our experts that are free and open to all faculty and graduate students. Join our LATIS Research Workshops Google Group to be the first to learn about workshops. Joining the group is highly recommended as these workshops are popular and often fill to capacity. You can view the slides and materials from past workshops at the LATIS Workshop Materials website.


Summer 2019 R Workshop Series

Register here for the whole series or individual workshops. 

R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. Additionally, R is free and designed for reproducible research. This workshop series will teach you how to get started using R to clean, manipulate, summarize,  and visualize data. We will not cover statistical analysis. Rather, this series will focus on all the steps that come *before* you run statistics, because getting your data into the right format is often the hardest part of data analysis. We will also cover tools for reproducible research, introducing you to R Markdown, a tool to tie together analysis and text into a report, and sharing other tips to keep your code and files organized.

While this workshop is open to participants from all disciplines, we will focus on issues social scientists often encounter when using data in R.

To be successful, you should have:


Register for one or all of the summer workshops. 

Introduction to R

This workshop will teach you how to get started using R to explore and clean your data.

You will learn how to:

  • Create an R script (syntax/command file) to capture data cleaning steps in a reproducible way
  • Load a comma-delimited spreadsheet (.csv) into R as a dataset
  • View and examine data in R
  • Check and correct missing values, rename variables, create new variables, and recode values in the data
  • Save cleaned data file in formats for later use in R or other applications

Manipulating data using dplyr

This workshop will introduce you to the dplyr package designed for data manipulation in R.

You will learn how to:

  • Subset a dataset to select the column/variables you need
  • Filter rows of the dataset to include only certain cases
  • Sort data by values in a column/variable
  • Chain together multiple R functions in a single command
  • Group and summarize data using descriptive statistics

Visualizing data with ggplot2

Ggplot2 is a popular package that extends R’s capability for data visualization, allowing users to produce attractive and complex graphics in a relatively simple way. This workshop will introduce the logic behind ggplot2 and give users hands-on experience creating data visualizations using this package.

You will learn how to:

  • Understand the basics of the "grammar of graphics" underlying ggplot2's functionality
  • Create a variety of reproducible data visualizations in R, such as histograms, line charts, scatter plots, heatmaps, and density plots
  • Visualize data by groups in multiple ways, including color labeling and faceting

Reshaping and Merging data

Often data are not in the shape or format you need for analysis. This workshop will teach you how to reshape data using the reshape2 package and how to merge multiple files into a single dataset.

Your will learn how to:

  • Transform data from "wide" (single row per individual case; many columns) to "long" (multiple rows per individual case; fewer columns) and vice versa.
  • Check for and remove duplicates in a file before merging.
  • Examine cases that are present in each dataset before merging and determine the overlap
  • Learn about and perform different types of merges depending on what cases you want to keep in your final dataset.

Using R Markdown for Reproducible Research

This workshop explores strategies for creating reproducible reports integrating R code and output using Markdown. Learn how to integrate citations, table of contents, publication-worthy tables, and LaTeX packages into R Markdown for extra functionality.

You will learn how to:

  • Create reproducible reports using RMarkdown, including how to format text in markdown, inserting and formatting chunks of R code and output, and other add ons, such as table of contents and bibliographies.
  • Learn tips for ensuring your analysis can be understood by others (or future you!) and repeated without error.

Before this workshop, please:

  • Install the packages rmarkdown and knitr

R Consultation Session

Get help or advice on your own projects from R experts around camps. Drop in!